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Scientific prediction of precipitation changes has important guiding value and significance for revealing regional spatial and temporal patterns of precipitation changes, flood climate prediction, etc. Based on the fact that CEEMD can effectively overcome the interference of modal aliasing and white noise, fine composite multi-scale entropy can reorganize the same FCMSE value to reduce the modal component and improve the computational efficiency, and Stacking ensemble learning can effectively and conveniently improve the fitting effect of machine learning, a rainfall prediction method based on CEEMD-fine composite multi-scale entropy and Stacking ensemble learning is constructed, and it is applied to the prediction of monthly precipitation in the Xixia. The results show that, under the same conditions, the CEEMD-RCMSE-Stacking model reduces the root mean square error by 83.48% and 62.08%, and the mean absolute error by 83.25% and 61.84%, respectively, compared with the single Stacking model and CEEMD-LSTM, while the goodness-of-fit coefficients improve by 15.94% and 2.34%, respectively, which means that the CEEMD-RCMSE-Stacking model has higher prediction performance. The CEEMD-RCMSE-Stacking model has higher prediction performance.

Accurate medium and long-term runoff forecasts play a vital role in guiding the rational exploitation of water resources and improving the overall efficiency of water resources use. Machine learning is becoming a common trend in time series forecasting research. Least squares support vector machine (LSSVM) and grey model (GM(1,1)) have received much attention in predicting rainfall and runoff in the last two years. “Decomposition-forecasting” has become one of the most important methods for forecasting time series data. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition method has powerful advantages in dealing with nonlinear data. Least squares support vector machine (LSSVM) has strong nonlinear fitting ability and good robustness. Gray model (GM(1,1)) can solve the problems of little historical data and low serial integrity and reliability. Based on their respective advantages, a combined CEEMDAN–LSSVM–GM(1,1) model was developed and applied to the runoff prediction of the lower Yellow River. To verify the reliability of the model, the prediction results were compared with the single LSSVM model, the CEEMDAN–LSSVM model and the CEEMDAN–support vector machines (SVM)–GM(1,1). The results show that the combined CEEMDAN–LSSVM–GM(1,1) model has a high accuracy and the prediction results are better than other models, which provides an effective prediction method for regional medium and long-term runoff prediction and has good application prospects.

Scientific assessment of the hydrodynamic conditions of water system connection is of great significance and value in guiding the ecological construction of urban water system connection and building healthy river–lake relationship. Based on the advantages of SWMM in effectively simulating urban rainfall and surface runoff, a hydrological and hydrodynamic model was constructed before and after the implementation of urban water system connection project, and the model was applied to the analysis of rainfall and flooding processes in two recurrence periods of 5a and 10a after the implementation of urban water system connection in Pingyu County. The results show that the initial flow rate and velocity of river cross-section increased in the same recurrence period after the water system connection, the maximum increase of water exchange capacity was 115.56%, the peak flow rate and velocity were weakened and the peak time was lagged, and the hydrodynamic conditions were improved significantly.

Industrial water consumption is a major component of the total regional water consumption. Accurate and scientific prediction of industrial water consumption is an essential guide to the rational use of natural resources. In this paper, we proposed a combined model of CEEMD (collective empirical modal decomposition) and ARIMA (autoregressive integrated moving average) for forecasting industrial water consumption to establish an accurate and efficient forecasting model, because of the poor generalization ability of most current industrial water consumption forecasting models. The influencing factors of industrial water consumption are complex, and the data are non-stationary. "Decomposition-prediction-reconstruction" is one of the significant methods for forecasting time series data, and the data decomposition has a suppressive influence on the modal mixing problem in the EMD decomposition procedure. Based on the smoothing ability of CEEMD for non-smooth signals and the better adaptation of the autoregressive moving average prediction model (ARIMA), a combined CEEMD-ARIMA model was established for industrial water consumption forecasting. This study was conducted for industrial water consumption in Henan Province in central China. The results suggest the combined CEEMD-ARIMA model has a favorable forecasting effect, with an average relative percentage error of 1.96%, and mean square error (MSE) of 0.35, a Nash efficiency coefficient (NSE) of 0.95, a prediction pass rate of 100%, and a better prediction accuracy than the ARIMA model and the combined EEMD-ARIMA model. It provides an effective prediction method for the prediction of industrial water consumption and has good application prospects.

Dengzhou City is the first ecological water recharge city after the commissioning of the South-North Water Diversion Project. A study on the ecological service value of Dengzhou City and its evolutionary characteristics can provide a theoretical basis for the water supply security of the Central Line Project and regional ecological environmental protection. Based on the ecosystem service value equivalent factor method, GIS, ENVI and GeoSOS-FLUS software were used to monetize the ecosystem service values of Dengzhou City in 2007, 2011, 2014, 2015 and 2020; analyze the spatial and temporal change characteristics under its land use change conditions and predict the regional ecosystem service values of Dengzhou City in 2025. The results show that since the completion and commissioning of the South-North Water Transfer Central Project in 2014, the value of ecosystem services in Dengzhou City has rapidly increased from US$3.792 billion to US$4.612 billion, and in 2025, the value of ecosystem services in Dengzhou City is expected to reach US$4.8 billion.

Analyzing and evaluating the ecosystem service value of wetlands can provide technical support and theoretical basis for regional socio-economic development, ecological environmental protection, and other planning measures. Based on the factors of hydrology, meteorology, society, economy, resources, environment, and culture, the wetland ecosystem services value of Yellow River Delta (YRD) was evaluated by the method of functional value and equivalent factor, which has certain reference significance for future research. Two methods were used to calculate the ecosystem services value of YRD in 2016, and the results were $3.28 × 109 per year and $4.53 × 109 per year, respectively, and the regulation services accounted for 76.3% and 16.7%, respectively. Through comparative analysis of ecosystem services value from 2001 to 2016, the average proportion of regulation services was 51%. Regulation service is the leading function of wetland ecosystem of YRD. Equivalent factor method is more suitable for the assessment of wetland ecosystem service value of YRD in 2016. In terms of time, ecosystem services value tended to increase, with an average rate of $2.02 × 109 per year. In terms of space, it gradually increased from inland to shallow sea water. On the premise of rational development and utilization, it is predicted that the value of ecosystem services will continue to grow in the future.

We analysed the temporal and spatial transcript expression of the panel of 18 putative isoallergens from four gene families (Pru p 1–4) in the peach fruit, anther and leaf of two melting cultivars, to gain insight into their expression profiles and to identify the key family members. Genotypic variation of abundantly expressed genes in mature fruit was further screened in nine additional melting cultivars. In the Pru p 1 family, Pru p 1.01 and Pru p 1.06B were predominant and constitutively expressed in all the tissues, with large difference among cultivars observed in mature fruits. Pru p 1.02 was especially abundant only in the leaf. A new member of the Pru p 1 family, Pru p 1.06D, was identified through peach genome mining. In the Pru p 2 family, Pru p 2.01B was predominant in all tissues, whereas Pru p 2.01A was abundant in a few cultivars and undetectable in others. Pru p 3.01 was the most highly expressed member in all tissues except the mesocarp, while the other two members exhibited tissue specificity: Pru p 3.02 was highly expressed in the leaf, and Pru p 3.03 in the anther. Both Pru p 4 isoallergen genes were equally expressed in all tissues of both cultivars. There was high expression variability of Pru p 1 and Pru p 2 members in mature fruits among 11 cultivars, while relative lower for Pru p 3 and Pru p 4. The location, arrangement and features of peach isoallergen genes on the peach genome scaffolds were illustrated.

Salidroside (p-hydroxyphenethyl-beta-d-glucoside), which is present in all species of the genus Rhodiola, has been reported to have a broad spectrum of pharmacological properties. The present study, for the first time, focused on evaluating the effects of the purified salidroside on the proliferation of various human cancer cell lines derived from different tissues, and further investigating its possible molecular mechanisms. Cell viability assay and [(3)H] thymidine incorporation were used to evaluate the cytotoxic effects of salidroside on cancer cell lines, and flow cytometry analyzed the change of cell cycle distribution induced by salidroside. Western immunoblotting further studied the expression changes of cyclins (cyclin D1 and cyclin B1), cyclin-dependent kinases (CDK4 and Cdc2), and cyclin-dependent kinase inhibitors (p21(Cip1) and p27(Kip1)). The results showed that salidroside inhibited the growth of various human cancer cell lines in concentration- and time-dependent manners, and the sensitivity to salidroside was different in those cancer cell lines. Salidroside could cause G1-phase or G2-phase arrest in different cancer cell lines, meanwhile, salidroside resulted in a decrease of CDK4, cyclin D1, cyclin B1 and Cdc2, and upregulated the levels of p27(Kip1) and p21(Cip1). Taken together, salidroside could inhibit the growth of cancer cells by modulating CDK4-cyclin D1 pathway for G1-phase arrest and/or modulating the Cdc2-cyclin B1 pathway for G2-phase arrest.